Police officers have always played a vital role in enforcing laws and reducing crime and disorder, while improving the overall quality of life for the community.
Sadly, it is found that the occupational fatality rate for law enforcement is three to five times greater than the national average for the working population, which has brought to the national consciousness concerns among many researchers, law enforcement agencies, and policymakers:
What are the major causes of police death? When and where do these tragedies happen the most frequently? Which states need the most attention and care? How should we allocate resources and build policies to protect the officers from death, so they can safely protect our communities?
To help answer these questions, InetSoft built an interactive Police Force Deaths Analysis Dashboard that displays police fatality data since 1791, across 17,985 police departments in the United States. The visualization's charts and tables let users comprehend these vast amounts of data at a glance in a more efficient way.
InetSoft Style Intelligence has rich built-in visual components and multiple functionalities which allow users to visualize data in diverse styles of charts, aggregates KPIs, and filters. InetSoft's Law Enforcement Analytics are a great example of this.
The dashboard utilizes area charts to show the death trends over time, allowing users to explore how police deaths have evolved over the years. It includes a map chart showing each state's death count, allowing the users to compare across states with a quick scan of the visualization.
The dashboard also incorporates several tables to allow users to dive deep into more details. The tables list the top 15 death causes, the top 15 types of police officers in the most danger, and the top 15 departments with the most police deaths, providing details of police deaths that are the most relevant to analyze from an abundant amount of data.
The dashboard also contains bar charts that sort months and weekdays by death counts, providing users another dimension to slice and dice data for deeper investigation. All the tables and charts can be used as filters that allow agile drilldowns and filtering of data, which further saves users time by reducing the number of clicks and the amount of time taken to search and change filters.
InetSoft's application is designed to maximize user self-service. With InetSoft's Police Force Deaths Analytics Dashboard, government users can easily cater the completed dashboard to specific needs and conduct advanced and detailed analysis in no time.
For example, by utilizing the cause of death checklist, they can use the dashboard to investigate how specific events like 9/11 and COVID-19 affected police death rates and compare the severity across states. This way, they can provide support and allocate limited resources in the most efficient way possible. By clicking on different months/weekdays, they can uncover the potential seasonal patterns and understand how different levels of attention should be paid over time.
To make the most use of the maps, they can click and drag to select multiple states across the map, immediately filtering the whole dashboard not only by a single state but by multiple states at once. For example, they might be interested in analyzing the police force death rates across the whole Northeast region.
The Police Force Deaths Analytics Dashboard is an example visualization created by InetSoft's Style Intelligence, a web-based application for mashup-driven dashboards and reports. It aims to improve efficiency and transparency across all levels of government by allowing agencies and municipalities to analyze and share volumes of information with the public and other agencies in a clear and concise manner.
With InetSoft Style Intelligence, a data source only needs to be linked once, and dashboard views can be scheduled for automatic updates to incorporate incoming information. This means that government users can always refresh the dashboard with the newest information to spot trends and aberrations across time and location, monitor the effects of local policies, estimate the causal effects of the interventions through comparisons of relevant outcomes across regions over time, and gather instant insights for building up new policies and making the best possible budget decisions.
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